Coordinate with breeders at 5 breeding locations across the US (South Carolina, Oregon, West Virginia, New York, and Minnesota) to understand their breeding objectives, phenotyping needs, and manage project deliverables.
Work with PIs and breeders at USDA centers and universities to develop genomic breeding strategies, specify use-cases that clearly spell out the computational and interface requirements of the breeders, and form project plans to develop key capabilities and integrate Breeding Insight into the breeding programs.
Overall, combines these communications into a regular process coordinated with the development cycle implemented by the software team to ensure a well-defined roadmap of new methods and features.
In communication with breeders, designs and helps execute evaluation experiments and analyses of features introduced to the Breeding Insight on which follow-up requirement documents are based.
The Communications Lead will have a leading role in communicating the goals, project plans, progress, online tutorials, and documentation for the tools. The lead will also be responsible for getting systematic feedback from the breeding group clients. An annual meeting among the IT team and breeders will used in some years to collect feedback and set direction. The communications lead with work closely with the
developers and coordinators to robust learning environment that the breeding teams can access.
The Quality Assurance Specialist will ensure quality software processes are followed throughout the system--from designing unit and integration tests to ensuring that the software is meeting the needs of the coordinators and breeding groups.
The USDA, Agricultural Research Service, United States Vegetable Laboratory in Charleston, South Carolina, is seeking a POSTDOCTORAL RESEARCH ASSOCIATE, (Research Computational Biologist) for a TWO YEAR APPOINTMENT. Ph.D. is required. The salary is $64,009 per annum plus benefits. Citizenship restrictions apply.
The responsibilities of the position
Coordinate the development of data pipelines for high-throughput plant phenotyping and image analyses that will enhance specialty crop breeding programs at the USVL.
Develop image analysis and machine learning algorithms to facilitate the quantification of plant tolerance to biotic and abiotic stresses.
Develop data management protocols and systems that are capable of handling large volumes and different varieties of data, and work with a software development team to help develop graphical user interface (GUI) and standard application programming interface (API) for these systems.
Operation of various imaging systems and platforms, such as RGB, hyperspectral, and microscope-based.
Provide technical support, guidance, and training in image analysis and machine learning to USVL researchers.
Lead implementation and application of novel machine learning methodologies in plant phenotyping for vegetable crops at the USVL in collaboration with Breeding Insight. Breeding Insight is funded by the USDA, ARS through Cornell University.
Ph.D. degree in Agricultural and Biosystems engineering, computer/electronic engineering, information technology, computational biology, and other relevant background or proven hands-on experience in image analysis and machine learning.
Knowledge and experience in multiple programming languages and platforms (e.g. Python, C/C++, Fortran, Matlab, PlantCV, ImageJ, R).
Excellent communication and collaboration skills to work with plant biologists, IT support teams, engineers and algorithm developers.
Familiar with processing and analyzing RGB and hyperspectral imagery.
Strong knowledge and experience in data management, specifically in database applications, complex web applications, and storage technologies.
Familiarity and experience of applying statistical models to image processing a plus.
Qualified persons are requested to send a letter of application including a 1-page (maximum) statement of research goals, and a curriculum vitae as electronic PDFs along with email addresses for three references, and one representative example of their scholarly work to: